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Modeling Graphene Extraction Process Using Generative Diffusion Models

EasyChair Preprint no. 9782

16 pagesDate: February 26, 2023


Graphene, a two-dimensional material composed of carbon atoms arranged in a hexagonal lattice, possesses a unique array of proper- ties that make it a highly sought-after material for a wide range of appli- cations. Its extraction process, a chemical reaction’s result is represented as an image which shows areas of synthesized material. Knowing initial conditions (oxidizer) the synthesis result could be modeled by generating possible visual outcome. A novel text2image pipeline to generate exper- imental images from chemical oxidizers are proposed. Key components of such pipeline are a textual input encoder and a conditional generative model. In this work the capabilities of certain text model and generative diffusion model are investigated and some conclusions are drawn provid- ing further suggestions for further full text2image pipeline development.

Keyphrases: CLIP, diffusion, Graphene, text2image

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Modestas Grazys},
  title = {Modeling Graphene Extraction Process Using Generative Diffusion Models},
  howpublished = {EasyChair Preprint no. 9782},

  year = {EasyChair, 2023}}
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